Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China

Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on dia...

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Main Authors:	Zhijin Wang, Yaohui Huang, Bingyan He, Ting Luo, Yongming Wang, Yonggang Fu
Format:	Article
Language:	English
Published:	Hindawi Limited 2020-01-01
Series:	Scientific Programming
Online Access:	http://dx.doi.org/10.1155/2020/8814222

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spelling	doaj-83a0cdb9875446938caddb1b78fa4a3e2021-07-02T14:50:21ZengHindawi LimitedScientific Programming1058-92441875-919X2020-01-01202010.1155/2020/88142228814222Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, ChinaZhijin Wang0Yaohui Huang1Bingyan He2Ting Luo3Yongming Wang4Yonggang Fu5Computer Engineering College, Jimei University, Xiamen 361021, ChinaChengyi University College, Jimei University, Xiamen 361021, ChinaComputer Engineering College, Jimei University, Xiamen 361021, ChinaChengyi University College, Jimei University, Xiamen 361021, ChinaChina Electronics Technology Group Corporation, Shanghai 200001, ChinaComputer Engineering College, Jimei University, Xiamen 361021, ChinaInfectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.http://dx.doi.org/10.1155/2020/8814222
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Zhijin Wang Yaohui Huang Bingyan He Ting Luo Yongming Wang Yonggang Fu
spellingShingle	Zhijin Wang Yaohui Huang Bingyan He Ting Luo Yongming Wang Yonggang Fu Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China Scientific Programming
author_facet	Zhijin Wang Yaohui Huang Bingyan He Ting Luo Yongming Wang Yonggang Fu
author_sort	Zhijin Wang
title	Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China
title_short	Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China
title_full	Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China
title_fullStr	Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China
title_full_unstemmed	Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China
title_sort	short-term infectious diarrhea prediction using weather and search data in xiamen, china
publisher	Hindawi Limited
series	Scientific Programming
issn	1058-9244 1875-919X
publishDate	2020-01-01
description	Infectious diarrhea has high morbidity and mortality around the world. For this reason, diarrhea prediction has emerged as an important problem to prevent and control outbreaks. Numerous studies have built disease prediction models using large-scale data. However, these methods perform poorly on diarrhea data. To address this issue, this paper proposes a parsimonious model (PM), which takes historical outpatient visit counts, meteorological factors (MFs) and Baidu search indices (BSIs) as inputs to perform prediction. An experimental evaluation was done to compare the short-term prediction performance of ten algorithms for four groups of inputs, using data collected in Xiamen, China. Results show that the proposed method is effective in improving the prediction accuracy.
url	http://dx.doi.org/10.1155/2020/8814222
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Short-Term Infectious Diarrhea Prediction Using Weather and Search Data in Xiamen, China

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